Overview

Dataset statistics

Number of variables38
Number of observations1184
Missing cells0
Missing cells (%)0.0%
Total size in memory273.0 KiB
Average record size in memory236.1 B

Variable types

Numeric17
Text21

Alerts

Unnamed: 0 has unique valuesUnique
key_id has unique valuesUnique
match_id has unique valuesUnique
group_stage has 310 (26.2%) zerosZeros
knockout_stage has 874 (73.8%) zerosZeros
replayed has 1180 (99.7%) zerosZeros
replay has 1180 (99.7%) zerosZeros
home_team_score has 262 (22.1%) zerosZeros
away_team_score has 415 (35.1%) zerosZeros
home_team_score_margin has 238 (20.1%) zerosZeros
away_team_score_margin has 238 (20.1%) zerosZeros
extra_time has 1101 (93.0%) zerosZeros
penalty_shootout has 1146 (96.8%) zerosZeros
home_team_score_penalties has 1147 (96.9%) zerosZeros
away_team_score_penalties has 1146 (96.8%) zerosZeros
home_team_win has 513 (43.3%) zerosZeros
away_team_win has 871 (73.6%) zerosZeros
draw has 984 (83.1%) zerosZeros

Reproduction

Analysis started2023-10-23 21:36:25.145821
Analysis finished2023-10-23 21:36:25.937821
Duration0.79 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIQUE 

Distinct1184
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean591.5
Minimum0
Maximum1183
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-10-23T23:36:26.106279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile59.15
Q1295.75
median591.5
Q3887.25
95-th percentile1123.85
Maximum1183
Range1183
Interquartile range (IQR)591.5

Descriptive statistics

Standard deviation341.9356665
Coefficient of variation (CV)0.5780822764
Kurtosis-1.2
Mean591.5
Median Absolute Deviation (MAD)296
Skewness0
Sum700336
Variance116920
MonotonicityStrictly increasing
2023-10-23T23:36:26.456764image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.1%
795 1
 
0.1%
793 1
 
0.1%
792 1
 
0.1%
791 1
 
0.1%
790 1
 
0.1%
789 1
 
0.1%
788 1
 
0.1%
787 1
 
0.1%
786 1
 
0.1%
Other values (1174) 1174
99.2%
ValueCountFrequency (%)
0 1
0.1%
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
ValueCountFrequency (%)
1183 1
0.1%
1182 1
0.1%
1181 1
0.1%
1180 1
0.1%
1179 1
0.1%

key_id
Real number (ℝ)

UNIQUE 

Distinct1184
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean592.5
Minimum1
Maximum1184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-10-23T23:36:26.750639image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile60.15
Q1296.75
median592.5
Q3888.25
95-th percentile1124.85
Maximum1184
Range1183
Interquartile range (IQR)591.5

Descriptive statistics

Standard deviation341.9356665
Coefficient of variation (CV)0.5771066101
Kurtosis-1.2
Mean592.5
Median Absolute Deviation (MAD)296
Skewness0
Sum701520
Variance116920
MonotonicityStrictly increasing
2023-10-23T23:36:27.071449image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
796 1
 
0.1%
794 1
 
0.1%
793 1
 
0.1%
792 1
 
0.1%
791 1
 
0.1%
790 1
 
0.1%
789 1
 
0.1%
788 1
 
0.1%
787 1
 
0.1%
Other values (1174) 1174
99.2%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
ValueCountFrequency (%)
1184 1
0.1%
1183 1
0.1%
1182 1
0.1%
1181 1
0.1%
1180 1
0.1%
Distinct29
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-10-23T23:36:27.318427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters8288
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWC-1930
2nd rowWC-1930
3rd rowWC-1930
4th rowWC-1930
5th rowWC-1930
ValueCountFrequency (%)
wc-2006 64
 
5.4%
wc-2018 64
 
5.4%
wc-2002 64
 
5.4%
wc-2014 64
 
5.4%
wc-1998 64
 
5.4%
wc-2010 64
 
5.4%
wc-1986 52
 
4.4%
wc-1994 52
 
4.4%
wc-1990 52
 
4.4%
wc-2019 52
 
4.4%
Other values (19) 592
50.0%
2023-10-23T23:36:27.745677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
W 1184
14.3%
C 1184
14.3%
- 1184
14.3%
1 1050
12.7%
9 1000
12.1%
0 900
10.9%
2 668
8.1%
8 323
 
3.9%
6 212
 
2.6%
4 197
 
2.4%
Other values (3) 386
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4736
57.1%
Uppercase Letter 2368
28.6%
Dash Punctuation 1184
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1050
22.2%
9 1000
21.1%
0 900
19.0%
2 668
14.1%
8 323
 
6.8%
6 212
 
4.5%
4 197
 
4.2%
5 161
 
3.4%
7 140
 
3.0%
3 85
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
W 1184
50.0%
C 1184
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 1184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5920
71.4%
Latin 2368
 
28.6%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1184
20.0%
1 1050
17.7%
9 1000
16.9%
0 900
15.2%
2 668
11.3%
8 323
 
5.5%
6 212
 
3.6%
4 197
 
3.3%
5 161
 
2.7%
7 140
 
2.4%
Latin
ValueCountFrequency (%)
W 1184
50.0%
C 1184
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 1184
14.3%
C 1184
14.3%
- 1184
14.3%
1 1050
12.7%
9 1000
12.1%
0 900
10.9%
2 668
8.1%
8 323
 
3.9%
6 212
 
2.6%
4 197
 
2.4%
Other values (3) 386
 
4.7%
Distinct29
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-10-23T23:36:27.974093image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length27
Median length25
Mean length25.47972973
Min length25

Characters and Unicode

Total characters30168
Distinct characters28
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1930 FIFA Men's World Cup
2nd row1930 FIFA Men's World Cup
3rd row1930 FIFA Men's World Cup
4th row1930 FIFA Men's World Cup
5th row1930 FIFA Men's World Cup
ValueCountFrequency (%)
fifa 1184
20.0%
world 1184
20.0%
cup 1184
20.0%
men's 900
15.2%
women's 284
 
4.8%
2014 64
 
1.1%
1998 64
 
1.1%
2006 64
 
1.1%
2002 64
 
1.1%
2018 64
 
1.1%
Other values (24) 864
14.6%
2023-10-23T23:36:28.390634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4736
 
15.7%
F 2368
 
7.8%
o 1468
 
4.9%
W 1468
 
4.9%
r 1184
 
3.9%
s 1184
 
3.9%
u 1184
 
3.9%
C 1184
 
3.9%
d 1184
 
3.9%
l 1184
 
3.9%
Other values (18) 13024
43.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11224
37.2%
Uppercase Letter 8288
27.5%
Space Separator 4736
15.7%
Decimal Number 4736
15.7%
Other Punctuation 1184
 
3.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1468
13.1%
r 1184
10.5%
s 1184
10.5%
u 1184
10.5%
d 1184
10.5%
l 1184
10.5%
n 1184
10.5%
e 1184
10.5%
p 1184
10.5%
m 284
 
2.5%
Decimal Number
ValueCountFrequency (%)
1 1050
22.2%
9 1000
21.1%
0 900
19.0%
2 668
14.1%
8 323
 
6.8%
6 212
 
4.5%
4 197
 
4.2%
5 161
 
3.4%
7 140
 
3.0%
3 85
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
F 2368
28.6%
W 1468
17.7%
C 1184
14.3%
A 1184
14.3%
I 1184
14.3%
M 900
 
10.9%
Space Separator
ValueCountFrequency (%)
4736
100.0%
Other Punctuation
ValueCountFrequency (%)
' 1184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19512
64.7%
Common 10656
35.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 2368
 
12.1%
o 1468
 
7.5%
W 1468
 
7.5%
r 1184
 
6.1%
s 1184
 
6.1%
u 1184
 
6.1%
C 1184
 
6.1%
d 1184
 
6.1%
l 1184
 
6.1%
n 1184
 
6.1%
Other values (6) 5920
30.3%
Common
ValueCountFrequency (%)
4736
44.4%
' 1184
 
11.1%
1 1050
 
9.9%
9 1000
 
9.4%
0 900
 
8.4%
2 668
 
6.3%
8 323
 
3.0%
6 212
 
2.0%
4 197
 
1.8%
5 161
 
1.5%
Other values (2) 225
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4736
 
15.7%
F 2368
 
7.8%
o 1468
 
4.9%
W 1468
 
4.9%
r 1184
 
3.9%
s 1184
 
3.9%
u 1184
 
3.9%
C 1184
 
3.9%
d 1184
 
3.9%
l 1184
 
3.9%
Other values (18) 13024
43.2%

match_id
Text

UNIQUE 

Distinct1184
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-10-23T23:36:28.721609image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters10656
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1184 ?
Unique (%)100.0%

Sample

1st rowM-1930-01
2nd rowM-1930-02
3rd rowM-1930-03
4th rowM-1930-04
5th rowM-1930-05
ValueCountFrequency (%)
m-1930-01 1
 
0.1%
m-1930-18 1
 
0.1%
m-1930-04 1
 
0.1%
m-1930-05 1
 
0.1%
m-1930-06 1
 
0.1%
m-1930-07 1
 
0.1%
m-1930-08 1
 
0.1%
m-1930-09 1
 
0.1%
m-1930-10 1
 
0.1%
m-1930-11 1
 
0.1%
Other values (1174) 1174
99.2%
2023-10-23T23:36:29.247431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2368
22.2%
1 1472
13.8%
0 1268
11.9%
M 1184
11.1%
9 1107
10.4%
2 1048
9.8%
4 439
 
4.1%
8 434
 
4.1%
3 372
 
3.5%
6 357
 
3.4%
Other values (2) 607
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7104
66.7%
Dash Punctuation 2368
 
22.2%
Uppercase Letter 1184
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1472
20.7%
0 1268
17.8%
9 1107
15.6%
2 1048
14.8%
4 439
 
6.2%
8 434
 
6.1%
3 372
 
5.2%
6 357
 
5.0%
5 355
 
5.0%
7 252
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 2368
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 1184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9472
88.9%
Latin 1184
 
11.1%

Most frequent character per script

Common
ValueCountFrequency (%)
- 2368
25.0%
1 1472
15.5%
0 1268
13.4%
9 1107
11.7%
2 1048
11.1%
4 439
 
4.6%
8 434
 
4.6%
3 372
 
3.9%
6 357
 
3.8%
5 355
 
3.7%
Latin
ValueCountFrequency (%)
M 1184
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 2368
22.2%
1 1472
13.8%
0 1268
11.9%
M 1184
11.1%
9 1107
10.4%
2 1048
9.8%
4 439
 
4.1%
8 434
 
4.1%
3 372
 
3.5%
6 357
 
3.4%
Other values (2) 607
 
5.7%
Distinct865
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-10-23T23:36:29.551775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length36
Median length32
Mean length19.66891892
Min length12

Characters and Unicode

Total characters23288
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique640 ?
Unique (%)54.1%

Sample

1st rowFrance vs Mexico
2nd rowUnited States vs Belgium
3rd rowYugoslavia vs Brazil
4th rowRomania vs Peru
5th rowArgentina vs France
ValueCountFrequency (%)
vs 1184
29.9%
germany 159
 
4.0%
brazil 143
 
3.6%
italy 95
 
2.4%
england 95
 
2.4%
sweden 91
 
2.3%
argentina 90
 
2.3%
united 86
 
2.2%
france 85
 
2.1%
states 83
 
2.1%
Other values (95) 1850
46.7%
2023-10-23T23:36:30.104085image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2777
 
11.9%
a 2535
 
10.9%
s 1655
 
7.1%
e 1549
 
6.7%
n 1509
 
6.5%
r 1331
 
5.7%
v 1319
 
5.7%
i 1249
 
5.4%
t 934
 
4.0%
l 908
 
3.9%
Other values (37) 7522
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17756
76.2%
Space Separator 2777
 
11.9%
Uppercase Letter 2755
 
11.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2535
14.3%
s 1655
9.3%
e 1549
 
8.7%
n 1509
 
8.5%
r 1331
 
7.5%
v 1319
 
7.4%
i 1249
 
7.0%
t 934
 
5.3%
l 908
 
5.1%
o 860
 
4.8%
Other values (15) 3907
22.0%
Uppercase Letter
ValueCountFrequency (%)
S 447
16.2%
C 258
9.4%
B 226
 
8.2%
N 210
 
7.6%
A 208
 
7.5%
G 193
 
7.0%
U 178
 
6.5%
I 158
 
5.7%
E 134
 
4.9%
P 112
 
4.1%
Other values (11) 631
22.9%
Space Separator
ValueCountFrequency (%)
2777
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20511
88.1%
Common 2777
 
11.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2535
 
12.4%
s 1655
 
8.1%
e 1549
 
7.6%
n 1509
 
7.4%
r 1331
 
6.5%
v 1319
 
6.4%
i 1249
 
6.1%
t 934
 
4.6%
l 908
 
4.4%
o 860
 
4.2%
Other values (36) 6662
32.5%
Common
ValueCountFrequency (%)
2777
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2777
 
11.9%
a 2535
 
10.9%
s 1655
 
7.1%
e 1549
 
6.7%
n 1509
 
6.5%
r 1331
 
5.7%
v 1319
 
5.7%
i 1249
 
5.4%
t 934
 
4.0%
l 908
 
3.9%
Other values (37) 7522
32.3%
Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-10-23T23:36:30.297626image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length18
Median length11
Mean length11.41554054
Min length5

Characters and Unicode

Total characters13516
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgroup stage
2nd rowgroup stage
3rd rowgroup stage
4th rowgroup stage
5th rowgroup stage
ValueCountFrequency (%)
group 868
37.2%
stage 868
37.2%
round 111
 
4.8%
of 105
 
4.5%
16 105
 
4.5%
quarter-finals 66
 
2.8%
semi-finals 36
 
1.5%
second 36
 
1.5%
final 34
 
1.5%
quarter-final 32
 
1.4%
Other values (3) 70
 
3.0%
2023-10-23T23:36:30.709091image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
g 1736
12.8%
a 1204
8.9%
r 1202
8.9%
1147
8.5%
o 1120
8.3%
e 1081
8.0%
u 1077
8.0%
s 1058
7.8%
t 1020
7.5%
p 895
6.6%
Other values (12) 1976
14.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11982
88.7%
Space Separator 1147
 
8.5%
Decimal Number 210
 
1.6%
Dash Punctuation 177
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
g 1736
14.5%
a 1204
10.0%
r 1202
10.0%
o 1120
9.3%
e 1081
9.0%
u 1077
9.0%
s 1058
8.8%
t 1020
8.5%
p 895
7.5%
n 331
 
2.8%
Other values (8) 1258
10.5%
Decimal Number
ValueCountFrequency (%)
6 105
50.0%
1 105
50.0%
Space Separator
ValueCountFrequency (%)
1147
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 177
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11982
88.7%
Common 1534
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
g 1736
14.5%
a 1204
10.0%
r 1202
10.0%
o 1120
9.3%
e 1081
9.0%
u 1077
9.0%
s 1058
8.8%
t 1020
8.5%
p 895
7.5%
n 331
 
2.8%
Other values (8) 1258
10.5%
Common
ValueCountFrequency (%)
1147
74.8%
- 177
 
11.5%
6 105
 
6.8%
1 105
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13516
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
g 1736
12.8%
a 1204
8.9%
r 1202
8.9%
1147
8.5%
o 1120
8.3%
e 1081
8.0%
u 1077
8.0%
s 1058
7.8%
t 1020
7.5%
p 895
6.6%
Other values (12) 1976
14.6%
Distinct16
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-10-23T23:36:30.884553image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length14
Median length7
Mean length8.868243243
Min length7

Characters and Unicode

Total characters10500
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowGroup 1
2nd rowGroup 4
3rd rowGroup 2
4th rowGroup 3
5th rowGroup 1
ValueCountFrequency (%)
group 868
36.7%
not 316
 
13.3%
applicable 316
 
13.3%
b 114
 
4.8%
a 114
 
4.8%
c 102
 
4.3%
d 90
 
3.8%
e 66
 
2.8%
f 66
 
2.8%
1 62
 
2.6%
Other values (7) 254
 
10.7%
2023-10-23T23:36:31.280434image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
p 1500
14.3%
o 1184
11.3%
1184
11.3%
G 904
8.6%
u 868
8.3%
r 868
8.3%
a 632
 
6.0%
l 632
 
6.0%
c 317
 
3.0%
e 316
 
3.0%
Other values (17) 2095
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7581
72.2%
Uppercase Letter 1491
 
14.2%
Space Separator 1184
 
11.3%
Decimal Number 244
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 1500
19.8%
o 1184
15.6%
u 868
11.4%
r 868
11.4%
a 632
8.3%
l 632
8.3%
c 317
 
4.2%
e 316
 
4.2%
n 316
 
4.2%
b 316
 
4.2%
Other values (2) 632
8.3%
Uppercase Letter
ValueCountFrequency (%)
G 904
60.6%
B 114
 
7.6%
A 114
 
7.6%
C 101
 
6.8%
D 90
 
6.0%
F 66
 
4.4%
E 66
 
4.4%
H 36
 
2.4%
Decimal Number
ValueCountFrequency (%)
1 62
25.4%
2 59
24.2%
3 56
23.0%
4 55
22.5%
6 6
 
2.5%
5 6
 
2.5%
Space Separator
ValueCountFrequency (%)
1184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9072
86.4%
Common 1428
 
13.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 1500
16.5%
o 1184
13.1%
G 904
10.0%
u 868
9.6%
r 868
9.6%
a 632
7.0%
l 632
7.0%
c 317
 
3.5%
e 316
 
3.5%
n 316
 
3.5%
Other values (10) 1535
16.9%
Common
ValueCountFrequency (%)
1184
82.9%
1 62
 
4.3%
2 59
 
4.1%
3 56
 
3.9%
4 55
 
3.9%
6 6
 
0.4%
5 6
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 1500
14.3%
o 1184
11.3%
1184
11.3%
G 904
8.6%
u 868
8.3%
r 868
8.3%
a 632
 
6.0%
l 632
 
6.0%
c 317
 
3.0%
e 316
 
3.0%
Other values (17) 2095
20.0%

group_stage
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7381756757
Minimum0
Maximum1
Zeros310
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-10-23T23:36:31.464818image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4398132812
Coefficient of variation (CV)0.5958111269
Kurtosis-0.8243679644
Mean0.7381756757
Median Absolute Deviation (MAD)0
Skewness-1.084908939
Sum874
Variance0.1934357223
MonotonicityNot monotonic
2023-10-23T23:36:31.663414image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 874
73.8%
0 310
 
26.2%
ValueCountFrequency (%)
0 310
 
26.2%
1 874
73.8%
ValueCountFrequency (%)
1 874
73.8%
0 310
 
26.2%

knockout_stage
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2618243243
Minimum0
Maximum1
Zeros874
Zeros (%)73.8%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-10-23T23:36:31.854942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4398132812
Coefficient of variation (CV)1.679802984
Kurtosis-0.8243679644
Mean0.2618243243
Median Absolute Deviation (MAD)0
Skewness1.084908939
Sum310
Variance0.1934357223
MonotonicityNot monotonic
2023-10-23T23:36:32.030689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 874
73.8%
1 310
 
26.2%
ValueCountFrequency (%)
0 874
73.8%
1 310
 
26.2%
ValueCountFrequency (%)
1 310
 
26.2%
0 874
73.8%

replayed
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003378378378
Minimum0
Maximum1
Zeros1180
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-10-23T23:36:32.180298image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.05805007375
Coefficient of variation (CV)17.18282183
Kurtosis292.2411211
Mean0.003378378378
Median Absolute Deviation (MAD)0
Skewness17.13906277
Sum4
Variance0.003369811062
MonotonicityNot monotonic
2023-10-23T23:36:32.350219image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 1180
99.7%
1 4
 
0.3%
ValueCountFrequency (%)
0 1180
99.7%
1 4
 
0.3%
ValueCountFrequency (%)
1 4
 
0.3%
0 1180
99.7%

replay
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003378378378
Minimum0
Maximum1
Zeros1180
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-10-23T23:36:32.505000image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.05805007375
Coefficient of variation (CV)17.18282183
Kurtosis292.2411211
Mean0.003378378378
Median Absolute Deviation (MAD)0
Skewness17.13906277
Sum4
Variance0.003369811062
MonotonicityNot monotonic
2023-10-23T23:36:32.672935image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 1180
99.7%
1 4
 
0.3%
ValueCountFrequency (%)
0 1180
99.7%
1 4
 
0.3%
ValueCountFrequency (%)
1 4
 
0.3%
0 1180
99.7%
Distinct467
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-10-23T23:36:33.003829image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters11840
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)8.4%

Sample

1st row1930-07-13
2nd row1930-07-13
3rd row1930-07-14
4th row1930-07-14
5th row1930-07-15
ValueCountFrequency (%)
1934-05-27 8
 
0.7%
1958-06-15 8
 
0.7%
1958-06-08 8
 
0.7%
1958-06-11 7
 
0.6%
1938-06-05 6
 
0.5%
1991-11-19 6
 
0.5%
1991-11-21 6
 
0.5%
1991-11-17 5
 
0.4%
1950-07-02 5
 
0.4%
1954-06-16 4
 
0.3%
Other values (457) 1121
94.7%
2023-10-23T23:36:33.621148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2456
20.7%
- 2368
20.0%
1 1719
14.5%
6 1218
10.3%
2 1172
9.9%
9 1162
9.8%
7 451
 
3.8%
8 423
 
3.6%
5 307
 
2.6%
4 306
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9472
80.0%
Dash Punctuation 2368
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2456
25.9%
1 1719
18.1%
6 1218
12.9%
2 1172
12.4%
9 1162
12.3%
7 451
 
4.8%
8 423
 
4.5%
5 307
 
3.2%
4 306
 
3.2%
3 258
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 2368
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2456
20.7%
- 2368
20.0%
1 1719
14.5%
6 1218
10.3%
2 1172
9.9%
9 1162
9.8%
7 451
 
3.8%
8 423
 
3.6%
5 307
 
2.6%
4 306
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2456
20.7%
- 2368
20.0%
1 1719
14.5%
6 1218
10.3%
2 1172
9.9%
9 1162
9.8%
7 451
 
3.8%
8 423
 
3.6%
5 307
 
2.6%
4 306
 
2.6%
Distinct46
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-10-23T23:36:33.865964image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5920
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.9%

Sample

1st row15:00
2nd row15:00
3rd row12:45
4th row14:50
5th row16:00
ValueCountFrequency (%)
21:00 156
13.2%
16:00 154
13.0%
17:00 102
 
8.6%
15:00 95
 
8.0%
18:00 87
 
7.3%
19:00 66
 
5.6%
20:30 61
 
5.2%
19:30 52
 
4.4%
12:00 52
 
4.4%
20:00 37
 
3.1%
Other values (36) 322
27.2%
2023-10-23T23:36:34.267557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1976
33.4%
: 1184
20.0%
1 1126
19.0%
2 343
 
5.8%
3 318
 
5.4%
5 258
 
4.4%
6 201
 
3.4%
7 157
 
2.7%
9 143
 
2.4%
4 110
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4736
80.0%
Other Punctuation 1184
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1976
41.7%
1 1126
23.8%
2 343
 
7.2%
3 318
 
6.7%
5 258
 
5.4%
6 201
 
4.2%
7 157
 
3.3%
9 143
 
3.0%
4 110
 
2.3%
8 104
 
2.2%
Other Punctuation
ValueCountFrequency (%)
: 1184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5920
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1976
33.4%
: 1184
20.0%
1 1126
19.0%
2 343
 
5.8%
3 318
 
5.4%
5 258
 
4.4%
6 201
 
3.4%
7 157
 
2.7%
9 143
 
2.4%
4 110
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1976
33.4%
: 1184
20.0%
1 1126
19.0%
2 343
 
5.8%
3 318
 
5.4%
5 258
 
4.4%
6 201
 
3.4%
7 157
 
2.7%
9 143
 
2.4%
4 110
 
1.9%
Distinct232
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-10-23T23:36:34.643896image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5920
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)1.2%

Sample

1st rowS-240
2nd rowS-239
3rd rowS-239
4th rowS-240
5th rowS-239
ValueCountFrequency (%)
s-132 19
 
1.6%
s-068 16
 
1.4%
s-020 15
 
1.3%
s-086 14
 
1.2%
s-128 14
 
1.2%
s-237 11
 
0.9%
s-131 11
 
0.9%
s-065 11
 
0.9%
s-024 11
 
0.9%
s-032 10
 
0.8%
Other values (222) 1052
88.9%
2023-10-23T23:36:35.206872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 1184
20.0%
- 1184
20.0%
0 831
14.0%
1 638
10.8%
2 533
9.0%
3 294
 
5.0%
6 245
 
4.1%
8 240
 
4.1%
9 206
 
3.5%
5 194
 
3.3%
Other values (2) 371
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3552
60.0%
Uppercase Letter 1184
 
20.0%
Dash Punctuation 1184
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 831
23.4%
1 638
18.0%
2 533
15.0%
3 294
 
8.3%
6 245
 
6.9%
8 240
 
6.8%
9 206
 
5.8%
5 194
 
5.5%
7 192
 
5.4%
4 179
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
S 1184
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4736
80.0%
Latin 1184
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1184
25.0%
0 831
17.5%
1 638
13.5%
2 533
11.3%
3 294
 
6.2%
6 245
 
5.2%
8 240
 
5.1%
9 206
 
4.3%
5 194
 
4.1%
7 192
 
4.1%
Latin
ValueCountFrequency (%)
S 1184
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 1184
20.0%
- 1184
20.0%
0 831
14.0%
1 638
10.8%
2 533
9.0%
3 294
 
5.0%
6 245
 
4.1%
8 240
 
4.1%
9 206
 
3.5%
5 194
 
3.3%
Other values (2) 371
 
6.3%
Distinct227
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-10-23T23:36:35.486924image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length30
Median length25
Mean length16.88766892
Min length6

Characters and Unicode

Total characters19995
Distinct characters68
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)1.1%

Sample

1st rowEstadio Pocitos
2nd rowEstadio Gran Parque Central
3rd rowEstadio Gran Parque Central
4th rowEstadio Pocitos
5th rowEstadio Gran Parque Central
ValueCountFrequency (%)
stadium 328
 
11.6%
estadio 203
 
7.2%
stade 106
 
3.8%
arena 82
 
2.9%
de 65
 
2.3%
stadio 60
 
2.1%
estádio 53
 
1.9%
la 43
 
1.5%
park 35
 
1.2%
center 31
 
1.1%
Other values (308) 1818
64.4%
2023-10-23T23:36:35.968827image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2204
 
11.0%
1640
 
8.2%
i 1483
 
7.4%
o 1409
 
7.0%
t 1343
 
6.7%
d 1251
 
6.3%
e 1229
 
6.1%
n 1000
 
5.0%
r 848
 
4.2%
s 809
 
4.0%
Other values (58) 6779
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15450
77.3%
Uppercase Letter 2820
 
14.1%
Space Separator 1640
 
8.2%
Dash Punctuation 52
 
0.3%
Other Punctuation 27
 
0.1%
Decimal Number 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2204
14.3%
i 1483
9.6%
o 1409
9.1%
t 1343
8.7%
d 1251
8.1%
e 1229
 
8.0%
n 1000
 
6.5%
r 848
 
5.5%
s 809
 
5.2%
l 768
 
5.0%
Other values (26) 3106
20.1%
Uppercase Letter
ValueCountFrequency (%)
S 663
23.5%
E 298
10.6%
C 250
 
8.9%
A 184
 
6.5%
P 180
 
6.4%
M 130
 
4.6%
F 114
 
4.0%
R 111
 
3.9%
N 111
 
3.9%
B 103
 
3.7%
Other values (16) 676
24.0%
Other Punctuation
ValueCountFrequency (%)
. 14
51.9%
' 13
48.1%
Decimal Number
ValueCountFrequency (%)
8 3
50.0%
6 3
50.0%
Space Separator
ValueCountFrequency (%)
1640
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18270
91.4%
Common 1725
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2204
 
12.1%
i 1483
 
8.1%
o 1409
 
7.7%
t 1343
 
7.4%
d 1251
 
6.8%
e 1229
 
6.7%
n 1000
 
5.5%
r 848
 
4.6%
s 809
 
4.4%
l 768
 
4.2%
Other values (52) 5926
32.4%
Common
ValueCountFrequency (%)
1640
95.1%
- 52
 
3.0%
. 14
 
0.8%
' 13
 
0.8%
8 3
 
0.2%
6 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19792
99.0%
None 203
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2204
 
11.1%
1640
 
8.3%
i 1483
 
7.5%
o 1409
 
7.1%
t 1343
 
6.8%
d 1251
 
6.3%
e 1229
 
6.2%
n 1000
 
5.1%
r 848
 
4.3%
s 809
 
4.1%
Other values (46) 6576
33.2%
None
ValueCountFrequency (%)
é 57
28.1%
á 57
28.1%
ã 27
13.3%
í 15
 
7.4%
ó 14
 
6.9%
Ã¥ 9
 
4.4%
ö 9
 
4.4%
ô 4
 
2.0%
ê 3
 
1.5%
à 3
 
1.5%
Other values (2) 5
 
2.5%
Distinct197
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-10-23T23:36:36.296852image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length16
Median length13
Mean length8.300675676
Min length4

Characters and Unicode

Total characters9828
Distinct characters67
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.8%

Sample

1st rowMontevideo
2nd rowMontevideo
3rd rowMontevideo
4th rowMontevideo
5th rowMontevideo
ValueCountFrequency (%)
city 27
 
1.9%
mexico 23
 
1.7%
paris 19
 
1.4%
montevideo 18
 
1.3%
guadalajara 17
 
1.2%
washington 17
 
1.2%
d.c 17
 
1.2%
rio 15
 
1.1%
de 15
 
1.1%
janeiro 15
 
1.1%
Other values (207) 1203
86.8%
2023-10-23T23:36:36.809176image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 995
 
10.1%
o 846
 
8.6%
e 836
 
8.5%
n 817
 
8.3%
r 641
 
6.5%
i 581
 
5.9%
l 438
 
4.5%
t 424
 
4.3%
u 381
 
3.9%
s 374
 
3.8%
Other values (57) 3495
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8156
83.0%
Uppercase Letter 1394
 
14.2%
Space Separator 202
 
2.1%
Other Punctuation 51
 
0.5%
Dash Punctuation 25
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 995
12.2%
o 846
10.4%
e 836
10.3%
n 817
10.0%
r 641
 
7.9%
i 581
 
7.1%
l 438
 
5.4%
t 424
 
5.2%
u 381
 
4.7%
s 374
 
4.6%
Other values (25) 1823
22.4%
Uppercase Letter
ValueCountFrequency (%)
M 167
 
12.0%
S 142
 
10.2%
C 107
 
7.7%
P 106
 
7.6%
B 103
 
7.4%
R 75
 
5.4%
G 71
 
5.1%
D 70
 
5.0%
L 69
 
4.9%
N 57
 
4.1%
Other values (18) 427
30.6%
Other Punctuation
ValueCountFrequency (%)
. 34
66.7%
, 17
33.3%
Space Separator
ValueCountFrequency (%)
202
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9550
97.2%
Common 278
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 995
 
10.4%
o 846
 
8.9%
e 836
 
8.8%
n 817
 
8.6%
r 641
 
6.7%
i 581
 
6.1%
l 438
 
4.6%
t 424
 
4.4%
u 381
 
4.0%
s 374
 
3.9%
Other values (53) 3217
33.7%
Common
ValueCountFrequency (%)
202
72.7%
. 34
 
12.2%
- 25
 
9.0%
, 17
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9709
98.8%
None 119
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 995
 
10.2%
o 846
 
8.7%
e 836
 
8.6%
n 817
 
8.4%
r 641
 
6.6%
i 581
 
6.0%
l 438
 
4.5%
t 424
 
4.4%
u 381
 
3.9%
s 374
 
3.9%
Other values (44) 3376
34.8%
None
ValueCountFrequency (%)
ó 25
21.0%
ä 13
10.9%
ã 12
10.1%
ñ 11
9.2%
ü 10
 
8.4%
Ã¥ 10
 
8.4%
ö 10
 
8.4%
á 7
 
5.9%
í 7
 
5.9%
É 6
 
5.0%
Other values (3) 8
 
6.7%
Distinct19
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-10-23T23:36:37.017466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length13
Median length12
Mean length7.30152027
Min length5

Characters and Unicode

Total characters8645
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUruguay
2nd rowUruguay
3rd rowUruguay
4th rowUruguay
5th rowUruguay
ValueCountFrequency (%)
germany 134
 
9.6%
france 134
 
9.6%
united 116
 
8.3%
states 116
 
8.3%
south 96
 
6.9%
brazil 86
 
6.2%
mexico 84
 
6.0%
italy 69
 
4.9%
africa 64
 
4.6%
russia 64
 
4.6%
Other values (11) 433
31.0%
2023-10-23T23:36:37.468842image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1143
 
13.2%
e 834
 
9.6%
n 805
 
9.3%
i 620
 
7.2%
t 577
 
6.7%
r 532
 
6.2%
S 351
 
4.1%
d 287
 
3.3%
c 282
 
3.3%
l 245
 
2.8%
Other values (25) 2969
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7037
81.4%
Uppercase Letter 1396
 
16.1%
Space Separator 212
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1143
16.2%
e 834
11.9%
n 805
11.4%
i 620
8.8%
t 577
 
8.2%
r 532
 
7.6%
d 287
 
4.1%
c 282
 
4.0%
l 245
 
3.5%
s 244
 
3.5%
Other values (11) 1468
20.9%
Uppercase Letter
ValueCountFrequency (%)
S 351
25.1%
C 142
10.2%
U 134
 
9.6%
F 134
 
9.6%
G 134
 
9.6%
A 102
 
7.3%
B 86
 
6.2%
M 84
 
6.0%
I 69
 
4.9%
R 64
 
4.6%
Other values (3) 96
 
6.9%
Space Separator
ValueCountFrequency (%)
212
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8433
97.5%
Common 212
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1143
13.6%
e 834
 
9.9%
n 805
 
9.5%
i 620
 
7.4%
t 577
 
6.8%
r 532
 
6.3%
S 351
 
4.2%
d 287
 
3.4%
c 282
 
3.3%
l 245
 
2.9%
Other values (24) 2757
32.7%
Common
ValueCountFrequency (%)
212
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8645
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1143
 
13.2%
e 834
 
9.6%
n 805
 
9.3%
i 620
 
7.2%
t 577
 
6.7%
r 532
 
6.2%
S 351
 
4.1%
d 287
 
3.3%
c 282
 
3.3%
l 245
 
2.8%
Other values (25) 2969
34.3%
Distinct83
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-10-23T23:36:37.737500image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4736
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)1.1%

Sample

1st rowT-30
2nd rowT-83
3rd rowT-87
4th rowT-61
5th rowT-03
ValueCountFrequency (%)
t-09 103
 
8.7%
t-31 64
 
5.4%
t-03 61
 
5.2%
t-41 61
 
5.2%
t-28 49
 
4.1%
t-74 47
 
4.0%
t-30 44
 
3.7%
t-83 44
 
3.7%
t-86 39
 
3.3%
t-73 36
 
3.0%
Other values (73) 636
53.7%
2023-10-23T23:36:38.187645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 1184
25.0%
- 1184
25.0%
3 348
 
7.3%
4 328
 
6.9%
0 318
 
6.7%
1 296
 
6.2%
8 252
 
5.3%
7 206
 
4.3%
6 173
 
3.7%
5 170
 
3.6%
Other values (2) 277
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2368
50.0%
Uppercase Letter 1184
25.0%
Dash Punctuation 1184
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 348
14.7%
4 328
13.9%
0 318
13.4%
1 296
12.5%
8 252
10.6%
7 206
8.7%
6 173
7.3%
5 170
7.2%
2 155
6.5%
9 122
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
T 1184
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3552
75.0%
Latin 1184
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1184
33.3%
3 348
 
9.8%
4 328
 
9.2%
0 318
 
9.0%
1 296
 
8.3%
8 252
 
7.1%
7 206
 
5.8%
6 173
 
4.9%
5 170
 
4.8%
2 155
 
4.4%
Latin
ValueCountFrequency (%)
T 1184
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4736
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1184
25.0%
- 1184
25.0%
3 348
 
7.3%
4 328
 
6.9%
0 318
 
6.7%
1 296
 
6.2%
8 252
 
5.3%
7 206
 
4.3%
6 173
 
3.7%
5 170
 
3.6%
Other values (2) 277
 
5.8%
Distinct83
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-10-23T23:36:38.476399image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length22
Median length20
Mean length7.763513514
Min length4

Characters and Unicode

Total characters9192
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)1.1%

Sample

1st rowFrance
2nd rowUnited States
3rd rowYugoslavia
4th rowRomania
5th rowArgentina
ValueCountFrequency (%)
germany 106
 
7.7%
brazil 103
 
7.4%
argentina 61
 
4.4%
italy 61
 
4.4%
england 49
 
3.5%
sweden 47
 
3.4%
united 45
 
3.3%
france 44
 
3.2%
states 44
 
3.2%
west 39
 
2.8%
Other values (88) 784
56.7%
2023-10-23T23:36:39.014847image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1238
 
13.5%
n 797
 
8.7%
e 777
 
8.5%
r 688
 
7.5%
i 613
 
6.7%
t 491
 
5.3%
l 451
 
4.9%
o 369
 
4.0%
g 269
 
2.9%
d 268
 
2.9%
Other values (37) 3231
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7619
82.9%
Uppercase Letter 1374
 
14.9%
Space Separator 199
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1238
16.2%
n 797
10.5%
e 777
10.2%
r 688
9.0%
i 613
 
8.0%
t 491
 
6.4%
l 451
 
5.9%
o 369
 
4.8%
g 269
 
3.5%
d 268
 
3.5%
Other values (15) 1658
21.8%
Uppercase Letter
ValueCountFrequency (%)
S 219
15.9%
B 134
9.8%
G 119
8.7%
A 117
8.5%
C 112
 
8.2%
N 99
 
7.2%
U 96
 
7.0%
I 83
 
6.0%
E 60
 
4.4%
P 53
 
3.9%
Other values (11) 282
20.5%
Space Separator
ValueCountFrequency (%)
199
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8993
97.8%
Common 199
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1238
13.8%
n 797
 
8.9%
e 777
 
8.6%
r 688
 
7.7%
i 613
 
6.8%
t 491
 
5.5%
l 451
 
5.0%
o 369
 
4.1%
g 269
 
3.0%
d 268
 
3.0%
Other values (36) 3032
33.7%
Common
ValueCountFrequency (%)
199
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1238
 
13.5%
n 797
 
8.7%
e 777
 
8.5%
r 688
 
7.5%
i 613
 
6.7%
t 491
 
5.3%
l 451
 
4.9%
o 369
 
4.0%
g 269
 
2.9%
d 268
 
2.9%
Other values (37) 3231
35.2%
Distinct82
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-10-23T23:36:39.281910image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3552
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)1.1%

Sample

1st rowFRA
2nd rowUSA
3rd rowYUG
4th rowROU
5th rowARG
ValueCountFrequency (%)
bra 103
 
8.7%
deu 103
 
8.7%
ita 61
 
5.2%
arg 61
 
5.2%
eng 49
 
4.1%
swe 47
 
4.0%
fra 44
 
3.7%
usa 44
 
3.7%
esp 36
 
3.0%
nld 33
 
2.8%
Other values (72) 603
50.9%
2023-10-23T23:36:39.752479image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 422
11.9%
R 414
11.7%
E 312
 
8.8%
U 301
 
8.5%
N 280
 
7.9%
S 213
 
6.0%
G 172
 
4.8%
D 168
 
4.7%
C 140
 
3.9%
B 139
 
3.9%
Other values (16) 991
27.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3552
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 422
11.9%
R 414
11.7%
E 312
 
8.8%
U 301
 
8.5%
N 280
 
7.9%
S 213
 
6.0%
G 172
 
4.8%
D 168
 
4.7%
C 140
 
3.9%
B 139
 
3.9%
Other values (16) 991
27.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 3552
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 422
11.9%
R 414
11.7%
E 312
 
8.8%
U 301
 
8.5%
N 280
 
7.9%
S 213
 
6.0%
G 172
 
4.8%
D 168
 
4.7%
C 140
 
3.9%
B 139
 
3.9%
Other values (16) 991
27.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 422
11.9%
R 414
11.7%
E 312
 
8.8%
U 301
 
8.5%
N 280
 
7.9%
S 213
 
6.0%
G 172
 
4.8%
D 168
 
4.7%
C 140
 
3.9%
B 139
 
3.9%
Other values (16) 991
27.9%
Distinct87
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-10-23T23:36:40.013877image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4736
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.6%

Sample

1st rowT-46
2nd rowT-06
3rd rowT-09
4th rowT-56
5th rowT-30
ValueCountFrequency (%)
t-28 46
 
3.9%
t-46 45
 
3.8%
t-74 44
 
3.7%
t-30 41
 
3.5%
t-09 40
 
3.4%
t-83 39
 
3.3%
t-41 34
 
2.9%
t-73 34
 
2.9%
t-03 29
 
2.4%
t-48 28
 
2.4%
Other values (77) 804
67.9%
2023-10-23T23:36:40.449735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 1184
25.0%
- 1184
25.0%
4 326
 
6.9%
1 293
 
6.2%
3 284
 
6.0%
0 258
 
5.4%
8 249
 
5.3%
7 239
 
5.0%
6 226
 
4.8%
2 212
 
4.5%
Other values (2) 281
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2368
50.0%
Uppercase Letter 1184
25.0%
Dash Punctuation 1184
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 326
13.8%
1 293
12.4%
3 284
12.0%
0 258
10.9%
8 249
10.5%
7 239
10.1%
6 226
9.5%
2 212
9.0%
5 209
8.8%
9 72
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
T 1184
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3552
75.0%
Latin 1184
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1184
33.3%
4 326
 
9.2%
1 293
 
8.2%
3 284
 
8.0%
0 258
 
7.3%
8 249
 
7.0%
7 239
 
6.7%
6 226
 
6.4%
2 212
 
6.0%
5 209
 
5.9%
Latin
ValueCountFrequency (%)
T 1184
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4736
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1184
25.0%
- 1184
25.0%
4 326
 
6.9%
1 293
 
6.2%
3 284
 
6.0%
0 258
 
5.4%
8 249
 
5.3%
7 239
 
5.0%
6 226
 
4.8%
2 212
 
4.5%
Other values (2) 281
 
5.9%
Distinct87
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-10-23T23:36:40.749189image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length22
Median length20
Mean length7.905405405
Min length4

Characters and Unicode

Total characters9360
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.6%

Sample

1st rowMexico
2nd rowBelgium
3rd rowBrazil
4th rowPeru
5th rowFrance
ValueCountFrequency (%)
germany 53
 
3.8%
england 46
 
3.3%
mexico 45
 
3.2%
sweden 44
 
3.2%
france 41
 
2.9%
united 41
 
2.9%
brazil 40
 
2.9%
states 39
 
2.8%
korea 35
 
2.5%
italy 34
 
2.4%
Other values (94) 976
70.0%
2023-10-23T23:36:41.305363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1297
 
13.9%
e 772
 
8.2%
n 712
 
7.6%
r 643
 
6.9%
i 636
 
6.8%
o 491
 
5.2%
l 457
 
4.9%
t 443
 
4.7%
d 312
 
3.3%
u 309
 
3.3%
Other values (37) 3288
35.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7769
83.0%
Uppercase Letter 1381
 
14.8%
Space Separator 210
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1297
16.7%
e 772
9.9%
n 712
9.2%
r 643
 
8.3%
i 636
 
8.2%
o 491
 
6.3%
l 457
 
5.9%
t 443
 
5.7%
d 312
 
4.0%
u 309
 
4.0%
Other values (15) 1697
21.8%
Uppercase Letter
ValueCountFrequency (%)
S 228
16.5%
C 146
10.6%
N 111
 
8.0%
B 92
 
6.7%
A 91
 
6.6%
U 82
 
5.9%
I 75
 
5.4%
G 74
 
5.4%
E 74
 
5.4%
P 59
 
4.3%
Other values (11) 349
25.3%
Space Separator
ValueCountFrequency (%)
210
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9150
97.8%
Common 210
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1297
14.2%
e 772
 
8.4%
n 712
 
7.8%
r 643
 
7.0%
i 636
 
7.0%
o 491
 
5.4%
l 457
 
5.0%
t 443
 
4.8%
d 312
 
3.4%
u 309
 
3.4%
Other values (36) 3078
33.6%
Common
ValueCountFrequency (%)
210
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1297
 
13.9%
e 772
 
8.2%
n 712
 
7.6%
r 643
 
6.9%
i 636
 
6.8%
o 491
 
5.2%
l 457
 
4.9%
t 443
 
4.7%
d 312
 
3.3%
u 309
 
3.3%
Other values (37) 3288
35.1%
Distinct86
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-10-23T23:36:41.605336image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3552
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.6%

Sample

1st rowMEX
2nd rowBEL
3rd rowBRA
4th rowPER
5th rowFRA
ValueCountFrequency (%)
deu 50
 
4.2%
eng 46
 
3.9%
mex 45
 
3.8%
swe 44
 
3.7%
fra 41
 
3.5%
bra 40
 
3.4%
usa 39
 
3.3%
ita 34
 
2.9%
esp 34
 
2.9%
arg 29
 
2.4%
Other values (76) 782
66.0%
2023-10-23T23:36:42.049841image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 394
 
11.1%
A 326
 
9.2%
E 304
 
8.6%
N 286
 
8.1%
U 267
 
7.5%
S 237
 
6.7%
C 187
 
5.3%
G 173
 
4.9%
L 150
 
4.2%
P 128
 
3.6%
Other values (16) 1100
31.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3552
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 394
 
11.1%
A 326
 
9.2%
E 304
 
8.6%
N 286
 
8.1%
U 267
 
7.5%
S 237
 
6.7%
C 187
 
5.3%
G 173
 
4.9%
L 150
 
4.2%
P 128
 
3.6%
Other values (16) 1100
31.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3552
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 394
 
11.1%
A 326
 
9.2%
E 304
 
8.6%
N 286
 
8.1%
U 267
 
7.5%
S 237
 
6.7%
C 187
 
5.3%
G 173
 
4.9%
L 150
 
4.2%
P 128
 
3.6%
Other values (16) 1100
31.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 394
 
11.1%
A 326
 
9.2%
E 304
 
8.6%
N 286
 
8.1%
U 267
 
7.5%
S 237
 
6.7%
C 187
 
5.3%
G 173
 
4.9%
L 150
 
4.2%
P 128
 
3.6%
Other values (16) 1100
31.0%

score
Text

Distinct53
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-10-23T23:36:42.643528image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.004222973
Min length3

Characters and Unicode

Total characters3557
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)1.0%

Sample

1st row4–1
2nd row3–0
3rd row2–1
4th row3–1
5th row1–0
ValueCountFrequency (%)
1–0 130
 
11.0%
2–1 127
 
10.7%
1–1 104
 
8.8%
0–1 91
 
7.7%
2–0 90
 
7.6%
0–0 79
 
6.7%
3–1 63
 
5.3%
1–2 57
 
4.8%
3–0 53
 
4.5%
0–2 46
 
3.9%
Other values (43) 344
29.1%
2023-10-23T23:36:43.143396image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
– 1183
33.3%
1 770
21.6%
0 680
19.1%
2 488
13.7%
3 243
 
6.8%
4 103
 
2.9%
5 38
 
1.1%
6 24
 
0.7%
7 19
 
0.5%
8 6
 
0.2%
Other values (2) 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2373
66.7%
Dash Punctuation 1184
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 770
32.4%
0 680
28.7%
2 488
20.6%
3 243
 
10.2%
4 103
 
4.3%
5 38
 
1.6%
6 24
 
1.0%
7 19
 
0.8%
8 6
 
0.3%
9 2
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
– 1183
99.9%
- 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3557
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
– 1183
33.3%
1 770
21.6%
0 680
19.1%
2 488
13.7%
3 243
 
6.8%
4 103
 
2.9%
5 38
 
1.1%
6 24
 
0.7%
7 19
 
0.5%
8 6
 
0.2%
Other values (2) 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2374
66.7%
Punctuation 1183
33.3%

Most frequent character per block

Punctuation
ValueCountFrequency (%)
– 1183
100.0%
ASCII
ValueCountFrequency (%)
1 770
32.4%
0 680
28.6%
2 488
20.6%
3 243
 
10.2%
4 103
 
4.3%
5 38
 
1.6%
6 24
 
1.0%
7 19
 
0.8%
8 6
 
0.3%
9 2
 
0.1%

home_team_score
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.818412162
Minimum0
Maximum13
Zeros262
Zeros (%)22.1%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-10-23T23:36:43.371725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1.5
Q33
95-th percentile5
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.702754621
Coefficient of variation (CV)0.9363964105
Kurtosis4.508973192
Mean1.818412162
Median Absolute Deviation (MAD)0.5
Skewness1.644213257
Sum2153
Variance2.899373301
MonotonicityNot monotonic
2023-10-23T23:36:43.599963image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 330
27.9%
2 279
23.6%
0 262
22.1%
3 165
13.9%
4 76
 
6.4%
6 22
 
1.9%
5 22
 
1.9%
7 16
 
1.4%
8 5
 
0.4%
10 3
 
0.3%
Other values (3) 4
 
0.3%
ValueCountFrequency (%)
0 262
22.1%
1 330
27.9%
2 279
23.6%
3 165
13.9%
4 76
 
6.4%
ValueCountFrequency (%)
13 1
 
0.1%
11 1
 
0.1%
10 3
0.3%
9 2
 
0.2%
8 5
0.4%

away_team_score
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.108108108
Minimum0
Maximum8
Zeros415
Zeros (%)35.1%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-10-23T23:36:43.826075image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.171715897
Coefficient of variation (CV)1.057402151
Kurtosis3.447182305
Mean1.108108108
Median Absolute Deviation (MAD)1
Skewness1.513677832
Sum1312
Variance1.372918142
MonotonicityNot monotonic
2023-10-23T23:36:44.059991image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 434
36.7%
0 415
35.1%
2 209
17.7%
3 77
 
6.5%
4 27
 
2.3%
5 16
 
1.4%
7 3
 
0.3%
6 2
 
0.2%
8 1
 
0.1%
ValueCountFrequency (%)
0 415
35.1%
1 434
36.7%
2 209
17.7%
3 77
 
6.5%
4 27
 
2.3%
ValueCountFrequency (%)
8 1
 
0.1%
7 3
 
0.3%
6 2
 
0.2%
5 16
1.4%
4 27
2.3%

home_team_score_margin
Real number (ℝ)

ZEROS 

Distinct21
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7103040541
Minimum-8
Maximum13
Zeros238
Zeros (%)20.1%
Negative298
Negative (%)25.2%
Memory size4.8 KiB
2023-10-23T23:36:44.252508image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-8
5-th percentile-3
Q1-1
median1
Q32
95-th percentile4
Maximum13
Range21
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.160436208
Coefficient of variation (CV)3.041565363
Kurtosis2.816328542
Mean0.7103040541
Median Absolute Deviation (MAD)1
Skewness0.5423274429
Sum841
Variance4.667484607
MonotonicityNot monotonic
2023-10-23T23:36:44.424562image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 300
25.3%
0 238
20.1%
2 170
14.4%
-1 162
13.7%
3 89
 
7.5%
-2 67
 
5.7%
-3 43
 
3.6%
4 35
 
3.0%
5 23
 
1.9%
6 14
 
1.2%
Other values (11) 43
 
3.6%
ValueCountFrequency (%)
-8 1
 
0.1%
-7 1
 
0.1%
-6 2
 
0.2%
-5 9
0.8%
-4 13
1.1%
ValueCountFrequency (%)
13 1
 
0.1%
11 1
 
0.1%
10 1
 
0.1%
9 4
0.3%
8 4
0.3%

away_team_score_margin
Real number (ℝ)

ZEROS 

Distinct21
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.7103040541
Minimum-13
Maximum8
Zeros238
Zeros (%)20.1%
Negative648
Negative (%)54.7%
Memory size4.8 KiB
2023-10-23T23:36:44.595003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-13
5-th percentile-4
Q1-2
median-1
Q31
95-th percentile3
Maximum8
Range21
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.160436208
Coefficient of variation (CV)-3.041565363
Kurtosis2.816328542
Mean-0.7103040541
Median Absolute Deviation (MAD)1
Skewness-0.5423274429
Sum-841
Variance4.667484607
MonotonicityNot monotonic
2023-10-23T23:36:44.765704image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
-1 300
25.3%
0 238
20.1%
-2 170
14.4%
1 162
13.7%
-3 89
 
7.5%
2 67
 
5.7%
3 43
 
3.6%
-4 35
 
3.0%
-5 23
 
1.9%
-6 14
 
1.2%
Other values (11) 43
 
3.6%
ValueCountFrequency (%)
-13 1
 
0.1%
-11 1
 
0.1%
-10 1
 
0.1%
-9 4
0.3%
-8 4
0.3%
ValueCountFrequency (%)
8 1
 
0.1%
7 1
 
0.1%
6 2
 
0.2%
5 9
0.8%
4 13
1.1%

extra_time
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07010135135
Minimum0
Maximum1
Zeros1101
Zeros (%)93.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-10-23T23:36:44.947432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2554256353
Coefficient of variation (CV)3.643662074
Kurtosis9.385095715
Mean0.07010135135
Median Absolute Deviation (MAD)0
Skewness3.371831156
Sum83
Variance0.06524225515
MonotonicityNot monotonic
2023-10-23T23:36:45.102775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 1101
93.0%
1 83
 
7.0%
ValueCountFrequency (%)
0 1101
93.0%
1 83
 
7.0%
ValueCountFrequency (%)
1 83
 
7.0%
0 1101
93.0%

penalty_shootout
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03209459459
Minimum0
Maximum1
Zeros1146
Zeros (%)96.8%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-10-23T23:36:45.276886image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1763258084
Coefficient of variation (CV)5.493940977
Kurtosis26.30707965
Mean0.03209459459
Median Absolute Deviation (MAD)0
Skewness5.316262296
Sum38
Variance0.03109079071
MonotonicityNot monotonic
2023-10-23T23:36:45.449949image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 1146
96.8%
1 38
 
3.2%
ValueCountFrequency (%)
0 1146
96.8%
1 38
 
3.2%
ValueCountFrequency (%)
1 38
 
3.2%
0 1146
96.8%
Distinct14
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-10-23T23:36:45.577615image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3552
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st row0-0
2nd row0-0
3rd row0-0
4th row0-0
5th row0-0
ValueCountFrequency (%)
0-0 1146
96.8%
3–4 7
 
0.6%
5–4 5
 
0.4%
3–2 5
 
0.4%
4–3 4
 
0.3%
4–2 3
 
0.3%
5–3 3
 
0.3%
4–1 2
 
0.2%
4–5 2
 
0.2%
1–3 2
 
0.2%
Other values (4) 5
 
0.4%
2023-10-23T23:36:45.913424image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2293
64.6%
- 1146
32.3%
– 38
 
1.1%
3 25
 
0.7%
4 24
 
0.7%
5 12
 
0.3%
2 9
 
0.3%
1 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2368
66.7%
Dash Punctuation 1184
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2293
96.8%
3 25
 
1.1%
4 24
 
1.0%
5 12
 
0.5%
2 9
 
0.4%
1 5
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 1146
96.8%
– 38
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3552
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2293
64.6%
- 1146
32.3%
– 38
 
1.1%
3 25
 
0.7%
4 24
 
0.7%
5 12
 
0.3%
2 9
 
0.3%
1 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3514
98.9%
Punctuation 38
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2293
65.3%
- 1146
32.6%
3 25
 
0.7%
4 24
 
0.7%
5 12
 
0.3%
2 9
 
0.3%
1 5
 
0.1%
Punctuation
ValueCountFrequency (%)
– 38
100.0%

home_team_score_penalties
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1123310811
Minimum0
Maximum5
Zeros1147
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-10-23T23:36:46.149456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6514565034
Coefficient of variation (CV)5.799432331
Kurtosis35.36824858
Mean0.1123310811
Median Absolute Deviation (MAD)0
Skewness5.966386127
Sum133
Variance0.4243955758
MonotonicityNot monotonic
2023-10-23T23:36:46.359774image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1147
96.9%
3 15
 
1.3%
4 11
 
0.9%
5 8
 
0.7%
1 2
 
0.2%
2 1
 
0.1%
ValueCountFrequency (%)
0 1147
96.9%
1 2
 
0.2%
2 1
 
0.1%
3 15
 
1.3%
4 11
 
0.9%
ValueCountFrequency (%)
5 8
0.7%
4 11
0.9%
3 15
1.3%
2 1
 
0.1%
1 2
 
0.2%

away_team_score_penalties
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1021959459
Minimum0
Maximum5
Zeros1146
Zeros (%)96.8%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-10-23T23:36:46.541734image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5962933481
Coefficient of variation (CV)5.834804332
Kurtosis38.07583554
Mean0.1021959459
Median Absolute Deviation (MAD)0
Skewness6.145322593
Sum121
Variance0.355565757
MonotonicityNot monotonic
2023-10-23T23:36:46.723740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1146
96.8%
4 13
 
1.1%
3 10
 
0.8%
2 8
 
0.7%
5 4
 
0.3%
1 3
 
0.3%
ValueCountFrequency (%)
0 1146
96.8%
1 3
 
0.3%
2 8
 
0.7%
3 10
 
0.8%
4 13
 
1.1%
ValueCountFrequency (%)
5 4
 
0.3%
4 13
1.1%
3 10
0.8%
2 8
0.7%
1 3
 
0.3%

result
Text

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-10-23T23:36:46.892874image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length13
Median length13
Mean length11.47972973
Min length4

Characters and Unicode

Total characters13592
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhome team win
2nd rowhome team win
3rd rowhome team win
4th rowhome team win
5th rowhome team win
ValueCountFrequency (%)
team 984
31.2%
win 984
31.2%
home 671
21.3%
away 313
 
9.9%
draw 200
 
6.3%
2023-10-23T23:36:47.289023image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1968
14.5%
a 1810
13.3%
m 1655
12.2%
e 1655
12.2%
w 1497
11.0%
t 984
7.2%
i 984
7.2%
n 984
7.2%
h 671
 
4.9%
o 671
 
4.9%
Other values (3) 713
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11624
85.5%
Space Separator 1968
 
14.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1810
15.6%
m 1655
14.2%
e 1655
14.2%
w 1497
12.9%
t 984
8.5%
i 984
8.5%
n 984
8.5%
h 671
 
5.8%
o 671
 
5.8%
y 313
 
2.7%
Other values (2) 400
 
3.4%
Space Separator
ValueCountFrequency (%)
1968
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11624
85.5%
Common 1968
 
14.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1810
15.6%
m 1655
14.2%
e 1655
14.2%
w 1497
12.9%
t 984
8.5%
i 984
8.5%
n 984
8.5%
h 671
 
5.8%
o 671
 
5.8%
y 313
 
2.7%
Other values (2) 400
 
3.4%
Common
ValueCountFrequency (%)
1968
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1968
14.5%
a 1810
13.3%
m 1655
12.2%
e 1655
12.2%
w 1497
11.0%
t 984
7.2%
i 984
7.2%
n 984
7.2%
h 671
 
4.9%
o 671
 
4.9%
Other values (3) 713
 
5.2%

home_team_win
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.566722973
Minimum0
Maximum1
Zeros513
Zeros (%)43.3%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-10-23T23:36:47.503241image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4957374393
Coefficient of variation (CV)0.8747438571
Kurtosis-1.930557011
Mean0.566722973
Median Absolute Deviation (MAD)0
Skewness-0.2696422183
Sum671
Variance0.2457556087
MonotonicityNot monotonic
2023-10-23T23:36:47.724774image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 671
56.7%
0 513
43.3%
ValueCountFrequency (%)
0 513
43.3%
1 671
56.7%
ValueCountFrequency (%)
1 671
56.7%
0 513
43.3%

away_team_win
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2643581081
Minimum0
Maximum1
Zeros871
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-10-23T23:36:47.889994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4411771621
Coefficient of variation (CV)1.668861853
Kurtosis-0.8564445122
Mean0.2643581081
Median Absolute Deviation (MAD)0
Skewness1.070049084
Sum313
Variance0.1946372884
MonotonicityNot monotonic
2023-10-23T23:36:48.040463image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 871
73.6%
1 313
 
26.4%
ValueCountFrequency (%)
0 871
73.6%
1 313
 
26.4%
ValueCountFrequency (%)
1 313
 
26.4%
0 871
73.6%

draw
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1689189189
Minimum0
Maximum1
Zeros984
Zeros (%)83.1%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-10-23T23:36:48.215928image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3748386141
Coefficient of variation (CV)2.219044596
Kurtosis1.133094697
Mean0.1689189189
Median Absolute Deviation (MAD)0
Skewness1.769515058
Sum200
Variance0.1405039867
MonotonicityNot monotonic
2023-10-23T23:36:48.387160image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 984
83.1%
1 200
 
16.9%
ValueCountFrequency (%)
0 984
83.1%
1 200
 
16.9%
ValueCountFrequency (%)
1 200
 
16.9%
0 984
83.1%